English Quality Assurance Lead

Role overview

This remote contractor position is for an English Quality Assurance Lead who will help maintain high standards across AI training work. You will review English-language AI outputs as well as trainer and QA submissions, compare them against project rules, and deliver clear written feedback so contributors can improve. The work focuses on checking accuracy, clarity, reasoning, grammar, tone, formatting, and compliance with each project’s rubric.

You will also look for repeated quality problems, share updates with trainers and QAs, support onboarding, keep reference materials current, and help re-engage contributors who are inactive. The role calls for excellent English, sharp attention to detail, and the ability to coordinate quality processes across distributed teams.

The company is an AI data services firm that supplies training data to major AI companies and foundation-model labs. Your work will directly contribute to improving AI systems by helping ensure training data is accurate, consistent, and aligned with client requirements.

Important: There is no immediate project tied to this role. If you are a fit, you may be contacted first when a relevant opportunity becomes available, and you may also gain access to future work through the expert network.

Employment details

This is a contract role that is remote and paid on an hourly basis.

Responsibilities

• Review submitted work, identify quality issues, and give ongoing feedback through direct messages while escalating serious or repeated concerns.
• Keep trainers and QAs informed on Discord about guideline updates, workflow adjustments, project changes, and expected quality standards.
• Answer contributor questions quickly and clearly so instructions and edge cases are well understood.
• Reach out to inactive contributors, encourage them to resume work, track follow-ups, and note any availability problems.
• Build and maintain documentation such as style guides, trackers, FAQs, quality notes, examples, honeypots, and onboarding resources.
• Run onboarding and training sessions to explain expectations, rubrics, workflows, and quality benchmarks.
• Make sure trainers and QAs apply the guidelines in a consistent way as projects change over time.
• Spot recurring weak points, suggest improvements to the process, and help develop scalable QA systems for future projects.

Requirements

• A bachelor’s or master’s degree in English, Linguistics, Communications, Journalism, Education, AI/Data Annotation, Quality Assurance, or a closely related field.
• Excellent command of English, including grammar, tone, structure, clarity, and the ability to provide strong written feedback.
• At least 3 years of professional experience in QA, editing, content review, AI training, education, annotation, localization QA, or a similar review-based role.
• Prior experience guiding remote teams of trainers, annotators, reviewers, editors, or QAs is strongly preferred.
• Ability to assess work using detailed rubrics, detect quality gaps, and explain issues in a constructive way.
• Strong written and verbal communication skills for answering questions, sharing updates, and giving actionable feedback.
• Comfort working in fast-paced remote environments using tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems.
• Strong organizational habits and attention to detail, with the ability to manage documentation and QA materials effectively.
• Dependable, self-driven, and capable of handling activation follow-ups, escalations, and quality checks across time zones.
• Exposure to AI training, data annotation, large language models, prompt/response evaluation, or rubric-based LLM QA is a plus.

Additional information

This is a fast-moving AI data services environment where the quality lead supports work for major AI organizations. The role is hourly, remote, and contractor-based. There is currently no immediate project, but suitable candidates may be prioritized for upcoming opportunities and future expert-network projects.